Nguyen H. Tran
Artificial Intelligence · 2019–2023
A researcher in the Science corpus: 8 papers; trusts 74 researchers and is trusted by 13 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 133 citations into it; #13,829 of 35,345 by trust here, #6,664 by citations
- Computer Networks and Communications — 96 citations into it
- Electrical and Electronic Engineering — 56 citations into it
- Federated & Distributed Learning — 56 citations into it; #138 of 319 by trust here, #111 by citations
- Computer Vision and Pattern Recognition — 47 citations into it
- Optimization & Training — 47 citations into it; #1,575 of 2,601 by trust here, #1,419 by citations
- Computational Mechanics — 23 citations into it
- Efficiency & Compression — 15 citations into it
Whom Nguyen H. Tran trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- H. Brendan McMahan
- Jakub Konecný
- Sebastian U. Stich
- Virginia Smith
- Geoffrey E. Hinton
- Alex Krizhevsky
- Yann LeCun
- Diederik P. Kingma
- Peter Richtárik
- Yurii E. Nesterov
Who trusts Nguyen H. Tran overall
- Mingzhe Chen
- Jiawen Kang
- Li Li
- Walid Saad
- Lumin Liu
- Zhaohui Yang
- Qinbin Li
- Latif Ullah Khan
- Wenqi Shi
- Dinh C. Nguyen
Papers from Nguyen H. Tran's perspective
Ordered by how strongly the researchers Nguyen H. Tran trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- Learning Multiple Layers of Features from Tiny Images
- ImageNet: A large-scale hierarchical image database
- Attention is All you Need (Neural Information Processing Systems)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Federated Learning over Wireless Networks: Optimization Model Design and Analysis (2019) — 911 citations
- Federated Learning for Edge Networks: Resource Optimization and Incentive Mechanism (2019) — 447 citations
- Intelligent Resource Slicing for eMBB and URLLC Coexistence in 5G and Beyond: A Deep Reinforcement Learning Based Approach (2020) — 307 citations
- A Crowdsourcing Framework for On-Device Federated Learning (2019) — 296 citations
- Revisiting Reverse Distillation for Anomaly Detection (2023) — 298 citations
- Personalized Federated Learning with Moreau Envelopes (2020) — 1,438 citations
- Federated Learning Over Wireless Networks: Convergence Analysis and Resource Allocation (2019) — 402 citations
- Edge-Computing-Enabled Smart Cities: A Comprehensive Survey (2019) — 390 citations